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<h1 id="sec_name">
<span data-if="hdevelop" style="display:inline;">diff_of_gauss</span><span data-if="c" style="display:none;">diff_of_gauss</span><span data-if="cpp" style="display:none;">DiffOfGauss</span><span data-if="dotnet" style="display:none;">DiffOfGauss</span><span data-if="python" style="display:none;">diff_of_gauss</span> (算子名称)</h1>
<h2>名称</h2>
<p><code><span data-if="hdevelop" style="display:inline;">diff_of_gauss</span><span data-if="c" style="display:none;">diff_of_gauss</span><span data-if="cpp" style="display:none;">DiffOfGauss</span><span data-if="dotnet" style="display:none;">DiffOfGauss</span><span data-if="python" style="display:none;">diff_of_gauss</span></code> — Approximate the LoG operator (Laplace of Gaussian).</p>
<h2 id="sec_synopsis">参数签名</h2>
<div data-if="hdevelop" style="display:inline;">
<p>
<code><b>diff_of_gauss</b>(<a href="#Image"><i>Image</i></a> : <a href="#DiffOfGauss"><i>DiffOfGauss</i></a> : <a href="#Sigma"><i>Sigma</i></a>, <a href="#SigFactor"><i>SigFactor</i></a> : )</code></p>
</div>
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<p>
<code>Herror <b>diff_of_gauss</b>(const Hobject <a href="#Image"><i>Image</i></a>, Hobject* <a href="#DiffOfGauss"><i>DiffOfGauss</i></a>, double <a href="#Sigma"><i>Sigma</i></a>, double <a href="#SigFactor"><i>SigFactor</i></a>)</code></p>
<p>
<code>Herror <b>T_diff_of_gauss</b>(const Hobject <a href="#Image"><i>Image</i></a>, Hobject* <a href="#DiffOfGauss"><i>DiffOfGauss</i></a>, const Htuple <a href="#Sigma"><i>Sigma</i></a>, const Htuple <a href="#SigFactor"><i>SigFactor</i></a>)</code></p>
</div>
<div data-if="cpp" style="display:none;">
<p>
<code>void <b>DiffOfGauss</b>(const HObject&amp; <a href="#Image"><i>Image</i></a>, HObject* <a href="#DiffOfGauss"><i>DiffOfGauss</i></a>, const HTuple&amp; <a href="#Sigma"><i>Sigma</i></a>, const HTuple&amp; <a href="#SigFactor"><i>SigFactor</i></a>)</code></p>
<p>
<code><a href="HImage.html">HImage</a> <a href="HImage.html">HImage</a>::<b>DiffOfGauss</b>(double <a href="#Sigma"><i>Sigma</i></a>, double <a href="#SigFactor"><i>SigFactor</i></a>) const</code></p>
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<p>
<code>static void <a href="HOperatorSet.html">HOperatorSet</a>.<b>DiffOfGauss</b>(<a href="HObject.html">HObject</a> <a href="#Image"><i>image</i></a>, out <a href="HObject.html">HObject</a> <a href="#DiffOfGauss"><i>diffOfGauss</i></a>, <a href="HTuple.html">HTuple</a> <a href="#Sigma"><i>sigma</i></a>, <a href="HTuple.html">HTuple</a> <a href="#SigFactor"><i>sigFactor</i></a>)</code></p>
<p>
<code><a href="HImage.html">HImage</a> <a href="HImage.html">HImage</a>.<b>DiffOfGauss</b>(double <a href="#Sigma"><i>sigma</i></a>, double <a href="#SigFactor"><i>sigFactor</i></a>)</code></p>
</div>
<div data-if="python" style="display:none;">
<p>
<code>def <b>diff_of_gauss</b>(<a href="#Image"><i>image</i></a>: HObject, <a href="#Sigma"><i>sigma</i></a>: float, <a href="#SigFactor"><i>sig_factor</i></a>: float) -&gt; HObject</code></p>
</div>
<h2 id="sec_description">描述</h2>
<p><code><span data-if="hdevelop" style="display:inline">diff_of_gauss</span><span data-if="c" style="display:none">diff_of_gauss</span><span data-if="cpp" style="display:none">DiffOfGauss</span><span data-if="com" style="display:none">DiffOfGauss</span><span data-if="dotnet" style="display:none">DiffOfGauss</span><span data-if="python" style="display:none">diff_of_gauss</span></code> approximates the Laplace-of-Gauss
operator by a difference of Gaussians.  The standard deviations of
these Gaussians can be calculated, according to Marr, from the
Parameter <a href="#Sigma"><i><code><span data-if="hdevelop" style="display:inline">Sigma</span><span data-if="c" style="display:none">Sigma</span><span data-if="cpp" style="display:none">Sigma</span><span data-if="com" style="display:none">Sigma</span><span data-if="dotnet" style="display:none">sigma</span><span data-if="python" style="display:none">sigma</span></code></i></a> of the LoG and the ratio of the two
standard deviations (<a href="#SigFactor"><i><code><span data-if="hdevelop" style="display:inline">SigFactor</span><span data-if="c" style="display:none">SigFactor</span><span data-if="cpp" style="display:none">SigFactor</span><span data-if="com" style="display:none">SigFactor</span><span data-if="dotnet" style="display:none">sigFactor</span><span data-if="python" style="display:none">sig_factor</span></code></i></a>) as:
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      <path style="fill-rule: evenodd;" d="M132.000000 -328.000000L132.000000 -352.000000C132.000000 -605.000000,256.000000 -641.000000,307.000000 -641.000000C331.000000 -641.000000,373.000000 -635.000000,395.000000 -601.000000C380.000000 -601.000000,340.000000 -601.000000,340.000000 -556.000000C340.000000 -525.000000,364.000000 -510.000000,386.000000 -510.000000C402.000000 -510.000000,432.000000 -519.000000,432.000000 -558.000000C432.000000 -618.000000,388.000000 -666.000000,305.000000 -666.000000C177.000000 -666.000000,42.000000 -537.000000,42.000000 -316.000000C42.000000 -49.000000,158.000000 22.000000,251.000000 22.000000C362.000000 22.000000,457.000000 -72.000000,457.000000 -204.000000C457.000000 -331.000000,368.000000 -427.000000,257.000000 -427.000000C189.000000 -427.000000,152.000000 -376.000000,132.000000 -328.000000M251.000000 -6.000000C188.000000 -6.000000,158.000000 -66.000000,152.000000 -81.000000C134.000000 -128.000000,134.000000 -208.000000,134.000000 -226.000000C134.000000 -304.000000,166.000000 -404.000000,256.000000 -404.000000C272.000000 -404.000000,318.000000 -404.000000,349.000000 -342.000000C367.000000 -305.000000,367.000000 -254.000000,367.000000 -205.000000C367.000000 -157.000000,367.000000 -107.000000,350.000000 -71.000000C320.000000 -11.000000,274.000000 -6.000000,251.000000 -6.000000"></path>
    </g>
    <g transform="scale(0.009963,0.009963)" id="GLYPHcmr10_61">
      <path style="fill-rule: evenodd;" d="M687.000000 -327.000000C702.000000 -327.000000,721.000000 -327.000000,721.000000 -347.000000C721.000000 -367.000000,702.000000 -367.000000,688.000000 -367.000000L89.000000 -367.000000C75.000000 -367.000000,56.000000 -367.000000,56.000000 -347.000000C56.000000 -327.000000,75.000000 -327.000000,90.000000 -327.000000M688.000000 -133.000000C702.000000 -133.000000,721.000000 -133.000000,721.000000 -153.000000C721.000000 -173.000000,702.000000 -173.000000,687.000000 -173.000000L90.000000 -173.000000C75.000000 -173.000000,56.000000 -173.000000,56.000000 -153.000000C56.000000 -133.000000,75.000000 -133.000000,89.000000 -133.000000"></path>
    </g>
    <g transform="scale(0.009963,0.009963)" id="GLYPHcmmi10_58">
      <path style="fill-rule: evenodd;" d="M192.000000 -53.000000C192.000000 -82.000000,168.000000 -106.000000,139.000000 -106.000000C110.000000 -106.000000,86.000000 -82.000000,86.000000 -53.000000C86.000000 -24.000000,110.000000 -0.000000,139.000000 -0.000000C168.000000 -0.000000,192.000000 -24.000000,192.000000 -53.000000"></path>
    </g>
  </defs>
  <g transform="matrix(1.600006,-0.000000,-0.000000,1.600006,0.158386 10.908966)" style="fill: rgb(0.000000%,0.000000%,0.000000%);">
    <use xlink:href="#GLYPHpcrr8r_83"></use>
    <use xlink:href="#GLYPHpcrr8r_105" x="6.000092"></use>
    <use xlink:href="#GLYPHpcrr8r_103" x="12.000185"></use>
    <use xlink:href="#GLYPHpcrr8r_70" x="18.000277"></use>
    <use xlink:href="#GLYPHpcrr8r_97" x="24.000369"></use>
    <use xlink:href="#GLYPHpcrr8r_99" x="30.000462"></use>
    <use xlink:href="#GLYPHpcrr8r_116" x="36.000554"></use>
    <use xlink:href="#GLYPHpcrr8r_111" x="42.000646"></use>
    <use xlink:href="#GLYPHpcrr8r_114" x="48.000739"></use>
  </g>
  <g transform="matrix(1.600006,-0.000000,-0.000000,1.600006,90.663544 10.908966)" style="fill: rgb(0.000000%,0.000000%,0.000000%);">
    <use xlink:href="#GLYPHcmr10_61"></use>
  </g>
  <g transform="matrix(1.600006,-0.000000,-0.000000,1.600006,107.489365 10.908966)" style="fill: rgb(0.000000%,0.000000%,0.000000%);">
    <use xlink:href="#GLYPHcmr10_49"></use>
  </g>
  <g transform="matrix(1.600006,-0.000000,-0.000000,1.600006,115.459625 10.908966)" style="fill: rgb(0.000000%,0.000000%,0.000000%);">
    <use xlink:href="#GLYPHcmmi10_58"></use>
  </g>
  <g transform="matrix(1.600006,-0.000000,-0.000000,1.600006,119.887482 10.908966)" style="fill: rgb(0.000000%,0.000000%,0.000000%);">
    <use xlink:href="#GLYPHcmr10_54"></use>
  </g>
</svg></span>, according
to Marr, an approximation to the Mexican-Hat-Operator results.  The
resulting image is stored in <a href="#DiffOfGauss"><i><code><span data-if="hdevelop" style="display:inline">DiffOfGauss</span><span data-if="c" style="display:none">DiffOfGauss</span><span data-if="cpp" style="display:none">DiffOfGauss</span><span data-if="com" style="display:none">DiffOfGauss</span><span data-if="dotnet" style="display:none">diffOfGauss</span><span data-if="python" style="display:none">diff_of_gauss</span></code></i></a>.</p>
<h2 id="sec_attention">注意</h2>
<p>Note that filter operators may return unexpected results if
an image with a reduced domain is used as input. Please refer to the
chapter <a href="toc_filters.html">过滤器</a>.</p>
<h2 id="sec_execution">运行信息</h2>
<ul>
  <li>多线程类型:可重入(与非独占操作符并行运行)。</li>
<li>多线程作用域:全局(可以从任何线程调用)。</li>
  
    <li>在元组级别自动并行化。</li>
    <li>在图像通道级别自动并行化。</li>
    <li>Automatically parallelized on  domain level.</li>
  
</ul>
<h2 id="sec_parameters">参数表</h2>
  <div class="par">
<div class="parhead">
<span id="Image" class="parname"><b><code><span data-if="hdevelop" style="display:inline">Image</span><span data-if="c" style="display:none">Image</span><span data-if="cpp" style="display:none">Image</span><span data-if="com" style="display:none">Image</span><span data-if="dotnet" style="display:none">image</span><span data-if="python" style="display:none">image</span></code></b> (input_object)  </span><span>(multichannel-)image(-array) <code>→</code> <span data-if="hdevelop" style="display:inline">object</span><span data-if="dotnet" style="display:none"><a href="HImage.html">HImage</a></span><span data-if="python" style="display:none">HObject</span><span data-if="cpp" style="display:none"><a href="HImage.html">HImage</a></span><span data-if="c" style="display:none">Hobject</span> (byte / uint2)</span>
</div>
<p class="pardesc">Input image</p>
</div>
  <div class="par">
<div class="parhead">
<span id="DiffOfGauss" class="parname"><b><code><span data-if="hdevelop" style="display:inline">DiffOfGauss</span><span data-if="c" style="display:none">DiffOfGauss</span><span data-if="cpp" style="display:none">DiffOfGauss</span><span data-if="com" style="display:none">DiffOfGauss</span><span data-if="dotnet" style="display:none">diffOfGauss</span><span data-if="python" style="display:none">diff_of_gauss</span></code></b> (output_object)  </span><span>(multichannel-)image(-array) <code>→</code> <span data-if="hdevelop" style="display:inline">object</span><span data-if="dotnet" style="display:none"><a href="HImage.html">HImage</a></span><span data-if="python" style="display:none">HObject</span><span data-if="cpp" style="display:none"><a href="HImage.html">HImage</a></span><span data-if="c" style="display:none">Hobject *</span> (int2)</span>
</div>
<p class="pardesc">LoG image.</p>
</div>
  <div class="par">
<div class="parhead">
<span id="Sigma" class="parname"><b><code><span data-if="hdevelop" style="display:inline">Sigma</span><span data-if="c" style="display:none">Sigma</span><span data-if="cpp" style="display:none">Sigma</span><span data-if="com" style="display:none">Sigma</span><span data-if="dotnet" style="display:none">sigma</span><span data-if="python" style="display:none">sigma</span></code></b> (input_control)  </span><span>real <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">float</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (real)</span><span data-if="dotnet" style="display:none"> (<i>double</i>)</span><span data-if="cpp" style="display:none"> (<i>double</i>)</span><span data-if="c" style="display:none"> (<i>double</i>)</span></span>
</div>
<p class="pardesc">Smoothing parameter of the Laplace operator to
approximate.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>3.0</p>
<p class="pardesc"><span class="parcat">Suggested values:
      </span>2.0, 3.0, 4.0, 5.0</p>
<p class="pardesc"><span class="parcat">Minimum increment:
      </span>0.01</p>
<p class="pardesc"><span class="parcat">Recommended increment:
      </span>0.1</p>
<p class="pardesc"><span class="parcat">Restriction:
      </span><code>Sigma &gt; 0.0</code></p>
</div>
  <div class="par">
<div class="parhead">
<span id="SigFactor" class="parname"><b><code><span data-if="hdevelop" style="display:inline">SigFactor</span><span data-if="c" style="display:none">SigFactor</span><span data-if="cpp" style="display:none">SigFactor</span><span data-if="com" style="display:none">SigFactor</span><span data-if="dotnet" style="display:none">sigFactor</span><span data-if="python" style="display:none">sig_factor</span></code></b> (input_control)  </span><span>real <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">float</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (real)</span><span data-if="dotnet" style="display:none"> (<i>double</i>)</span><span data-if="cpp" style="display:none"> (<i>double</i>)</span><span data-if="c" style="display:none"> (<i>double</i>)</span></span>
</div>
<p class="pardesc">Ratio of the standard deviations used (Marr
recommends 1.6).</p>
<p class="pardesc"><span class="parcat">Default:
      </span>1.6</p>
<p class="pardesc"><span class="parcat">Minimum increment:
      </span>0.01</p>
<p class="pardesc"><span class="parcat">Recommended increment:
      </span>0.1</p>
<p class="pardesc"><span class="parcat">Restriction:
      </span><code>SigFactor &gt; 0.0</code></p>
</div>
<div data-if="hdevelop" style="display:inline">
<h2 id="sec_example_hdevelop">例程 (HDevelop)</h2>
<pre class="example">
read_image(Image,'fabrik')
diff_of_gauss(Image,Laplace,2.0,1.6)
zero_crossing(Laplace,ZeroCrossings)
</pre>
</div>
<div data-if="c" style="display:none">
<h2 id="sec_example_c">例程 (C)</h2>
<pre class="example">
read_image(&amp;Image,"mreut");
diff_of_gauss(Image,&amp;Laplace,2.0,1.6);
zero_crossing(Laplace,&amp;ZeroCrossings);
</pre>
</div>
<div data-if="cpp" style="display:none">
<h2 id="sec_example_cpp">例程 (HDevelop)</h2>
<pre class="example">
read_image(Image,'fabrik')
diff_of_gauss(Image,Laplace,2.0,1.6)
zero_crossing(Laplace,ZeroCrossings)
</pre>
</div>
<div data-if="com" style="display:none">
<h2 id="sec_example_com">例程 (HDevelop)</h2>
<pre class="example">
read_image(Image,'fabrik')
diff_of_gauss(Image,Laplace,2.0,1.6)
zero_crossing(Laplace,ZeroCrossings)
</pre>
</div>
<div data-if="dotnet" style="display:none">
<h2 id="sec_example_dotnet">例程 (HDevelop)</h2>
<pre class="example">
read_image(Image,'fabrik')
diff_of_gauss(Image,Laplace,2.0,1.6)
zero_crossing(Laplace,ZeroCrossings)
</pre>
</div>
<h2 id="sec_complexity">Complexity</h2>
<p>The execution time depends linearly on the number of pixels and the
size of sigma.</p>
<h2 id="sec_result">结果</h2>
<p><code><span data-if="hdevelop" style="display:inline">diff_of_gauss</span><span data-if="c" style="display:none">diff_of_gauss</span><span data-if="cpp" style="display:none">DiffOfGauss</span><span data-if="com" style="display:none">DiffOfGauss</span><span data-if="dotnet" style="display:none">DiffOfGauss</span><span data-if="python" style="display:none">diff_of_gauss</span></code> returns <TT>2</TT> (
      <TT>H_MSG_TRUE</TT>)
     if all parameters are
correct.  If the input is empty the behavior can be set via
<a href="set_system.html"><code><span data-if="hdevelop" style="display:inline">set_system('no_object_result',&lt;Result&gt;)</span><span data-if="c" style="display:none">set_system("no_object_result",&lt;Result&gt;)</span><span data-if="cpp" style="display:none">SetSystem("no_object_result",&lt;Result&gt;)</span><span data-if="com" style="display:none">SetSystem("no_object_result",&lt;Result&gt;)</span><span data-if="dotnet" style="display:none">SetSystem("no_object_result",&lt;Result&gt;)</span><span data-if="python" style="display:none">set_system("no_object_result",&lt;Result&gt;)</span></code></a>.  If
necessary, an exception is raised.</p>
<h2 id="sec_successors">可能的后置算子</h2>
<p>
<code><a href="zero_crossing.html"><span data-if="hdevelop" style="display:inline">zero_crossing</span><span data-if="c" style="display:none">zero_crossing</span><span data-if="cpp" style="display:none">ZeroCrossing</span><span data-if="com" style="display:none">ZeroCrossing</span><span data-if="dotnet" style="display:none">ZeroCrossing</span><span data-if="python" style="display:none">zero_crossing</span></a></code>, 
<code><a href="dual_threshold.html"><span data-if="hdevelop" style="display:inline">dual_threshold</span><span data-if="c" style="display:none">dual_threshold</span><span data-if="cpp" style="display:none">DualThreshold</span><span data-if="com" style="display:none">DualThreshold</span><span data-if="dotnet" style="display:none">DualThreshold</span><span data-if="python" style="display:none">dual_threshold</span></a></code>
</p>
<h2 id="sec_alternatives">可替代算子</h2>
<p>
<code><a href="laplace.html"><span data-if="hdevelop" style="display:inline">laplace</span><span data-if="c" style="display:none">laplace</span><span data-if="cpp" style="display:none">Laplace</span><span data-if="com" style="display:none">Laplace</span><span data-if="dotnet" style="display:none">Laplace</span><span data-if="python" style="display:none">laplace</span></a></code>, 
<code><a href="derivate_gauss.html"><span data-if="hdevelop" style="display:inline">derivate_gauss</span><span data-if="c" style="display:none">derivate_gauss</span><span data-if="cpp" style="display:none">DerivateGauss</span><span data-if="com" style="display:none">DerivateGauss</span><span data-if="dotnet" style="display:none">DerivateGauss</span><span data-if="python" style="display:none">derivate_gauss</span></a></code>
</p>
<h2 id="sec_references">References</h2>
<p>

D. Marr: “Vision (A computational investigation into human
representation and processing of visual information)”; New York,
W.H. Freeman and Company; 1982.
</p>
<h2 id="sec_module">模块</h2>
<p>
Foundation</p>
<!--OP_REF_FOOTER_START-->
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